Faced with growing database storage needs, information technology (IT) departments are increasingly implementing networked storage, such as network-attached storage (NAS) and storage area networks (SANs), as well as direct-attach storage. Managing these heterogeneous and often distributed storage environments is typically a time-consuming manual task, requiring individual management of each storage device. In addition, the IT departments frequently experience “stranded” capacity, i.e., when one device cannot be accessed by applications that need the device, or capacity that is tied up in stale or wasted storage, resulting in inefficient resource utilization.
Managing large numbers of objects in database storage is a difficult and time-consuming task. Currently, large numbers of performance metric objects are managed using a brute force approach. The brute force approach uses multiple maps to access a particular object by descending down the maps until the object of interest is found. The mapping is done using a string name of the metric as a key in the map to the object of interest. Since devices may have sub-components that have performance metrics to be managed as well, multiple mapping processes are needed to manage the sub-components, which can be time-consuming and inefficient. For example, if an object has configuration information for a particular metric on a particular switch for a particular port, three mapping processes are needed to access the object of interest, as illustrated below:Metric1->Switch 1->Port 1->Configuration (object of interest)